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dc.contributor.authorPaulraj, M.P.-
dc.contributor.authorSazali, Yaacob-
dc.contributor.authorMohd Zubir, Md Zin-
dc.date.accessioned2009-11-17T01:44:40Z-
dc.date.available2009-11-17T01:44:40Z-
dc.date.issued2009-10-11-
dc.identifier.citationp.2B4 1 - 2B4 5en_US
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/7298-
dc.descriptionOrganized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.en_US
dc.description.abstractThe sound of working vehicle provides an important clue for engine faults diagnosis. Endless efforts have been put into the research of fault diagnosis based on sound. It offers concrete economic benefits, which can lead to high system reliability and save maintenance cost. A number of diagnostic systems for vehicle repair have been developing in recent years. Artificial Neural Network is a very demanding application and popularly implemented in many industries including condition monitoring via fault diagnosis. This paper presents a feature extraction algorithm using total entropy of 5 level decomposition of wavelet transform. The engine noise signal is decomposed into 5 levels (A5, D5, A4, D4, A3, D3, A2, D2, A1, D1) using Daubechies “db4” wavelet family. From the decomposed signals, the entropy is applied for each levels and the feature are extracted and used to develop a functional link neural network.en_US
dc.description.sponsorshipTechnical sponsored by IEEE Malaysia Sectionen_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlisen_US
dc.relation.ispartofseriesProceedings of the International Conference on Man-Machine Systems (ICoMMS 2009)en_US
dc.subjectEntropyen_US
dc.subjectWavelet analysisen_US
dc.subjectFunctional Link Neural Networken_US
dc.subjectEngine diagnosisen_US
dc.subjectWavelets (Mathematics)en_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectFault diagnosisen_US
dc.titleMotorbike engine faults diagnosing system using Entropy and Functional Link Neural Network in wavelet domainen_US
dc.typeWorking Paperen_US
Appears in Collections:Conference Papers
Sazali Yaacob, Prof. Dr.

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